Summaries and key ideas, written by Stratmill's research agent, of the books, papers, articles and code our AI agents read. Each page links to its original.
4,194 documents
This strategy selects a portfolio of NYSE stocks through liquidity, momentum, and return consistency screens. It excludes financial firms, ADRs, and REITs, then ranks eligible stocks by average dollar volume over 90 sessions. From that liquid subset, it…
This equity-selection strategy first screens tradable constituents of the China Securities Index 1000, excluding B-shares and stocks with negative price-to-earnings ratios. It sorts the remaining stocks by P/E, keeps the 1,000 lowest, then selects the five…
This document sketches a periodic stock-selection and execution process focused on the five smallest companies by market capitalization in the CSI 300 universe. On the first incoming market bar, the strategy selects the target basket and compares it with…
This framework example selects a changing equity universe by estimating each stock’s alpha relative to a benchmark. On a scheduled monthly selection date, it retrieves daily returns for a set of Dow constituents and the SPY benchmark, fits a linear…
This cross-sectional stock strategy ranks securities by price-to-book ratio, using book value per share calculated from total assets, total liabilities, and shares outstanding. It buys the lowest-ratio group and shorts the highest-ratio group, with the…
This example demonstrates a daily equity universe selection process using fundamental data. It filters for securities with available fundamental data and a price above $1, sorts the remaining names by daily dollar volume, then ranks them by P/E ratio and…
This walkthrough tests whether a stock’s unadjusted closing share price predicts its return over the following year. It describes preparing adjusted price data while retaining unadjusted closes, trading-volume information, and index membership, then sorting…
The document summarizes proposed cross-sectional signals for judging whether equity options are relatively cheap or expensive. Its central comparison is implied volatility against volatility that later realizes: options may be candidates to buy when implied…
The article frames long-term investing as earning compensation for bearing uncertainty. Stocks and bonds have historically risen over long periods, but their shorter-term losses and volatility help explain why investors expect a premium for holding them. It…
The document explains the fallen angel effect: bonds downgraded from investment grade to high yield may face forced selling from institutions whose mandates restrict junk bond holdings. That selling can push prices below their reduced fundamental value,…
This review surveys the research topics covered in Euan Sinclair’s book on positional option trading. It highlights potential sources of returns involving the implied volatility forward curve, cross-sectional equity option returns linked to fundamental…
The article examines momentum as a way to time exposure to a diversified risk-premia strategy. It describes measuring each asset’s trailing six-month return, ranking assets, and rotating into the top four with weights inversely related to their volatility…
The document describes cross-sectional signals for ranking equity options by potential volatility mispricing. It outlines value, company size, idiosyncratic volatility, beta convexity, implied volatility term structure, the implied versus realized volatility…
The article discusses two proposed crypto trading signals. The retail-flow factor uses order-book data to distinguish retail from institutional activity and treats unusually strong retail participation as a contrarian signal. The author reports a near-linear…
The document introduces factor analysis as a way to understand the sources of risk and return, then contrasts predefined equity factors with the less obvious drivers of returns across futures markets. It reviews possible uses of factors, including taking…
The article revisits a claim that CAPM explains returns across asset classes but not within them. It corrects the comparison by using futures returns as excess returns over the risk-free rate and distinguishing standard deviation from beta, which is based on…
This article surveys career paths in systematic trading and explains how roles differ across buy-side and sell-side firms. Buy-side organizations invest on behalf of clients or their own accounts, with analysts, traders, and portfolio managers contributing…
Unsupervised learning finds structure in observations without labeled outcomes. The article contrasts this with supervised prediction and explains that unlabeled methods are useful when labels are costly or data is high-dimensional. In finance, possible uses…
This lesson uses a factor model to separate portfolio risk into common factor risk and asset-specific risk. It constructs market, size, and value factor returns, estimates each stock’s exposure through regression, and explains how those exposures and factor…
The lecture presents a workflow for assessing whether an equity factor ranks stocks by future relative performance. Its momentum example measures price change over a long lookback while excluding the most recent period, then uses a filtered stock universe…
This lecture explains how the Capital Asset Pricing Model relates expected asset returns to a risk-free rate and exposure to broad market risk. It distinguishes diversifiable, firm-specific risk from systematic risk, and uses regression beta to estimate an…
The document explains how market beta and sector exposure can make a portfolio’s individual forecasts move together, reducing the number of independent bets and, in turn, its risk-adjusted potential. It frames this through the Fundamental Law of Active…
The document explains a cross-sectional long-short equity strategy: rank stocks with a model, buy the highest-ranked names, and short the lowest-ranked names using balanced dollar exposure. It presents the ranking signal as the strategy’s main source of…
This lecture uses factor models to explain portfolio returns and quantify exposure to systematic sources of risk. It describes regressing active returns, measured relative to a benchmark, on factor returns, then using estimated sensitivities and factor…